Predicting Breast Cancer with Ensemble Methods on Cloud

Au Pham; Tu Tran; Phuc Tran; Hiep Huynh

doi:10.4108/eetcasa.v8i2.2788

Authors

Au Pham Cai Be Technical College https://orcid.org/0000-0002-5244-2439
Tu Tran Vinh Long University of Technology Education, Vinh Long province, Vietnam https://orcid.org/0000-0001-5811-6952
Phuc Tran Department of Foreign Languages and Informatics People’s Police College II HCM city
Hiep Huynh Can Tho University, Can Tho city, Vietnam https://orcid.org/0000-0002-9213-131X

DOI:

https://doi.org/10.4108/eetcasa.v8i2.2788

Keywords:

Bagging, Boosting, Stacking, Random Forest, Ensemble methods

Abstract

There are many dangerous diseases and high mortality rates for women (including breast cancer). If the disease is detected early, correctly diagnosed and treated at the right time, the likelihood of illness and death is reduced. Previous disease prediction models have mainly focused on methods for building individual models. However, these predictive models do not yet have high accuracy and high generalization performance. In this paper, we focus on combining these individual models together to create a combined model, which is more generalizable than the individual models. Three ensemble techniques used in the experiment are: Bagging; Boosting and Stacking (Stacking include three models: Gradient Boost, Random Forest, Logistic Regression) to deploy and apply to breast cancer prediction problem. The experimental results show the combined model with the ensemble methods based on the Breast Cancer Wisconsin dataset; this combined model has a higher predictive performance than the commonly used individual prediction models.

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